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Research Progress in Solar Flare Prediction Methods

Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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Multi-view stereo algorithms based on deep learning: a survey

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Multimedia Tools and Applications 2025年第6期84卷 2877-2908页

作者： Huang, Hongbo Yan, Xiaoxu Zheng, Yaolin He, Jiayu Xu, Longfei Qin, Dechun Computer School Beijing Information Science & Technology University Beijing China Institute of Computing Intelligence Beijing Information Science & Technology University Beijing China Foreign Languages School Beijing Information Science & Technology University Beijing China

Multi-View Stereo (MVS) is a long-standing and fundamental task in computer vision, which aims to reconstruct the 3D geometry of a scene from a set of overlapping images. With known camera parameters, MVS matches pixels across images to compute dense correspondences and recover 3D points. Traditional MVS methods often encounter difficulties in addressing complex scenes due to limitations in robustness. However, recent advancements in deep-learning-based MVS have demonstrated remarkable performance gains, attributed to their enhanced feature extraction and cost volume processing capabilities. This comprehensive survey delves into deep-learning-based MVS algorithms, highlighting key challenges and avenues for future exploration. Our focus is on studies aimed at mitigating algorithmic memory requirements and elevating processing speeds. To better understand the developments in this field, we propose a new taxonomy. The deep-learning-based MVS algorithms are categorized into two categories: scene-oriented and view-oriented methods. Scene-oriented methods typically employ voxels or neural implicit representations as the representative form of the scene and infer the complete scene directly. View-oriented methods focus on estimating the depth map of a single view. Detailed classifications and reviews have been conducted for each category. Furthermore, we introduce several widely used datasets and metrics and empirical evaluation of representative algorithms across three popular datasets. This article concludes by discussing prevalent challenges in the MVS domain and promising research directions for the future. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Stereo image processing

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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NeurDB: an AI-powered autonomous data system

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science China(information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Intelligent Traffic Scheduling for Mobile Edge computing in IoT via Deep Learning

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Computer Modeling in Engineering & sciences 2023年第3期134卷 1815-1835页

作者： Shaoxuan Yun Ying Chen School of Computing Science Beijing Information Science and Technology UniversityBeijing100101China

Nowadays,with the widespread application of the Internet of Things(IoT),mobile devices are renovating our *** data generated by mobile devices has reached a massive *** traditional centralized processing is not suitable for processing the data due to limited computing power and transmission *** Edge computing(MEC)has been proposed to solve these *** of limited computation ability and battery capacity,tasks can be executed in the MEC ***,how to schedule those tasks becomes a challenge,and is the main topic of this *** this paper,we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in *** view of the advantages of deep learning,we propose a Deep Learning-Based Traffic Scheduling Approach(DLTSA).We translate the scheduling problem into a classification *** demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms.

关键词： Mobile Edge computing(MEC) traffic scheduling deep learning Internet of Things(IoT)

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua science and technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Performance Assessment of Optimized Link State Routing Protocol on Vehicular Ad Hoc Network Simulation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 289-302页

作者： Xian, Yap Yu Yogarayan, Sumendra Razak, Siti Fatimah Abdul Sayeed, Shohel Abdullah, Fikri Azli Kannan, Subarmaniam Azman, Afizan Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia School of Computing Faculty of Information and Technology Taylors University Selangor Subang Jaya Malaysia

Vehicular Ad-hoc Networks (VANETs) are dedicated forms of wireless communication networks designed to handle the challenges of vehicular environments, including high mobility, varying traffic densities, and constantly changing topologies. These factors necessitate the development and evaluation of routing protocols to ensure reliable data communication between vehicles. This study evaluates the performance of the Optimized Link State Routing (OLSR) protocol within Vehicular Ad-hoc Networks (VANETs), focusing on its capability to handle different traffic densities and dynamic environments. Reliable data communication in VANETs is critical due to the high mobility and constantly changing topologies, especially in urban and highway settings. Using NS-3 for network simulation and Simulation of Urban MObility (SUMO) for realistic vehicular mobility modelling, we conducted a series of simulations to assess OLSR’s performance in low-density and high-density scenarios across highway and urban environments. Key performance metrics, including packet delivery ratio (PDR), end-to-end delay (E2ED) and throughput were analyzed to capture OLSR’s strengths and weaknesses in each setting. The analysis showed that OLSR excels in low-density highway scenarios, achieving a PDR of 100% and low E2ED. However, in high-density urban settings, the protocol encounters performance challenges, with a reduced PDR of 81.40% and a high E2ED of 85.52 seconds, indicating delays in data transmission. These findings emphasize the limitations of OLSR in dense urban environments, highlighting the necessity for adaptive routing protocols that can improve performance in complex, high-density vehicular networks. © 2025, Ital Publication. All rights reserved.

关键词： Vehicular ad hoc networks

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PerFedKG: two-stage information-loop federated knowledge graph for personalized privacy-preserving recommendation systems

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science China(information sciences) 2025年第4期68卷 98-99页

作者： Fan WANG Xuyun ZHANG Weiming LIU Li LI Yuwen LIU Zhongyuan ZHANG Guanfeng LIU Shengye PANG Xiaolong XU Lianyong QI College of Computer Science and Technology Zhejiang University Department of Computing Macquarie University College of Computer Science and Technology China University of Petroleum (East China) School of Software Nanjing University of Information Science and Technology

Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.

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Optimizing AES S-Box Implementation:A SAT-Based Approach with Tower Field Representations

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Computers, Materials & Continua 2025年第4期83卷 1515-1531页

作者： Jingya Feng Ying Zhao Tao Ye Wei Feng School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Computing and Information Systems The University of MelbourneMelbourne3010Australia Guangxi Wangxin Information Technology Co. Ltd.Nanning530000China

The efficient implementation of the Advanced Encryption Standard(AES)is crucial for network data *** paper presents novel hardware implementations of the AES S-box,a core component,using tower field representations and Boolean Satisfiability(SAT)*** research makes several significant contri-butions to the ***,we have optimized the GF(24)inversion,achieving a remarkable 31.35%area reduction(15.33 GE)compared to the best known ***,we have enhanced multiplication implementa-tions for transformation matrices using a SAT-method based on local *** approach has yielded notable improvements,such as a 22.22%reduction in area(42.00 GE)for the top transformation matrix in GF((24)2)-type S-box ***,we have proposed new implementations of GF(((22)2)2)-type and GF((24)2)-type S-boxes,with the GF(((22)2)2)-type demonstrating superior *** implementation offers two variants:a small area variant that sets new area records,and a fast variant that establishes new benchmarks in Area-Execution-Time(AET)and energy *** approach significantly improves upon existing S-box implementations,offering advancements in area,speed,and energy *** optimizations contribute to more efficient and secure AES implementations,potentially enhancing various cryptographic applications in the field of network security.

关键词： AES S-box SAT optimization tower field hardware implementation area efficiency energy consumption

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